Recently, pattern recognition has received great deal of attention in diverse engineering fields such as oil exploration, biomedical imaging, speaker identification, automated data entry, finger prints recognition, etc. Many valuable contributions have been reported in these fields [see: Chung, K, Kee, S. C. and Kim S. R, “Face recognition using principal component analysis of Gabor filter responses”, Proceedings of 1999. International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-time Systems, pp. 53–57; Leondes, C. T.: Image Processing and Pattern Recognition, (Academic Press, 1998); and. Luo, X. and Mirchandani, G., “An integrated framework for image classification”, Proceedings of 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey, Vol. 1, pp. 620–623.].
Face recognition is one of the important research topics in this area which has been receiving the attention of many researchers due to its useful applications, such as system security and human-computer interface [Chellappa, R., Wilson, C. L. and Sirohey, S., “Human and machine recognition of faces”, a survey. Technical Report CAR-TR-731, CS-TR33339, University of Maryland, August 1994.]
In conventional pattern recognition, the task is divided into 2 parts. The first part is obtaining a feature space of reduced dimensions and complexity, and the second part is the classification of that space [Sarlashkar, M. N., Bodruzzaman, M. and Malkani, M. J., “Feature extraction using wavelet transform for neural network based image classification”, Proceedings of the Thirtieth Southeastern Symposium on System Theory, 1998, pp. 412–416.].
Neural Networks (NN) have been employed and compared to conventional classifiers for a number of classification problems. The results have shown that the accuracy of the NN approach is equivalent to, or slightly better than, other methods. Also, due to the simplicity and generality of the NN, it leads to classifiers that are more efficient [Zhou, W., “Verification of the nonparametric characteristics of back propagation neural networks for image classification”, IEEE Transactions on Geoscience and Remote Sensing, March 1999, Vol. 37, No. 2 pp. 771–779]. As reported in the literature, NN classifiers possess unique characteristics, some of which are:                (i) NN classifiers are distribution free. NNs allow the target classes to be defined without consideration to their distribution in the corresponding domain of each data source [Benediksson, J. A., Swain, P. H. and Ersoy, O. K., “Neural Network approaches versus statistical methods in classification of multisource remote sensing data”, IEEE Transaction on Geoscience and Remote Sensing, July 1990, Vol. 28, pp. 540–551.1]. In other words, using neural networks (NN) is a better choice when it is necessary to define heterogeneous classes that may cover extensive and irregularly formed areas in the spectral domain and may not be well described by statistical models;        (ii) NN classifiers are important free. When neural networks are used, data sources with different characteristics can be incorporated into the process of classification without knowing or specifying the weights on each data source. Until now, the importance-free of neural networks has mostly been demonstrated empirically [Bishof, H., Schneider, W. and Pinz, A. J., “Multispectral classification of LANDSAT-images using neural networks”, IEEE Trans. on Geoscience and Remote Sensing, May 1992, Vol. 30, pp. 482–490.]. Efforts have also been made to establish the relationship between the importance-free characteristic of neural networks and their internal structure, particularly their weights after training [see Zhou above]. In addition, NN implementations lend themselves to reduced storage and computational requirements.        
The NN learning is generally classified as supervised or unsupervised. Supervised methods have yielded higher accuracy than unsupervised ones, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes [Hara, Y., Atkins, R. G., Yueh, S. H., Shin, R. T., and Kong, J. A., “Application of Neural Networks to Radar Image Classification”, IEEE Trans. on Geoscience and Remote Sensing, January 1994, Vol. 32, No. 1, pp. 100–109 and Herman, P. D., and Khazenie, N., “Classification of multispectral remote sensing data using a back-propagation neural network”, IEEE Transactions on Geoscience and Remote Sensing, January 1992, Vol. 30, pp. 81–88.].
In the field of pattern recognition, the combination of an ensemble of neural networks has been to achieve image classification systems with higher performance in comparison with the best performance achievable employing a single neural network. This has been verified experimentally in the literature [Kittler, J., Hatef, M., Duin, R. P. W. and Matas, J., “On combining classifiers”, IEEE Transaction on Pattern Anaysis and Machine Intelligence, March 1998, Vol. 20, pp. 226–239.]. Also, it has been shown that additional advantages are provided by a neural network ensemble in the context of image classification applications. For example, the combination of neural networks can be used as a “data fusion” mechanism where different NN's process data from different sources [Luo, X. and Mirchandani, G., “An integrated framework for image classification”, Proceedings of 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey, Vol. 1, pp. 620–613.]. A number of image classification systems based on the combination of the outputs of a set of different classifiers has been. Different structures for combining classifiers can be grouped as follows [see Lu, Y. above and Ho, T. K., Hull, J. J. and Srihari, S. N., “Decision Combination in Multiple Classifier Systems”, IEEE Trans. on Pattern Analysis Machine Intelligence, January 1994, Vol. 16, No. 1, pp. 66–75.]:                i—Parallel Structure;        ii—Pipeline structure; and,        iii—Hierarchical structure.        
For the parallel structure, the classifiers are used in parallel and their outputs are combined. In the pipeline structure, the system classifiers are connected in cascade. The hierarchical structure is a combination of the structures in i & ii above.
The combination methods in the literature are based on voting rules, statistical techniques, belief functions and other classifier fusion schemes [Xu, L., Krzyzak, A. and Suen, C. Y., “Methods for combining multiple classifiers and their applications to handwriting recognition”, IEEE Trans. On Systems, Man and Cyb.22, May–June, 1992, Vol. 22, pp. 418–435., Prampero, P. S., and de Carvalho, A. C, “Recognition of Vehicles Silhouette using Combination of Classifiers”, International Joint Conference on Neural Networks, (IJCNN'98), 1998, pp. 1723–172613].
Another approach to pattern recognition is shown in U.S. Pat. No. 5,175,775 to Iwaki, et al. In its disclosure a vast number of reference images are grouped into initial groups each containing a limited number of the individual reference images in the first step. Then a most associated reference image having a maximum correlation coefficient is discriminated for each of the initial groups. Next in the second step, all of the thus obtained most-associated reference images are regrouped into new groups each having similarly a limited number of reference images. The number of new groups is accordingly small than that of the initial groups. The new groups are again subjected to the correlation operation to enable next regrouping. Lastly, in the third step, the number of groups is reduced to a single final group which contains less than the limited number of the reference images. The final group is subjected to the correlation operation with respect to the object image to thereby discriminate a particular reference image exactly corresponding to the object image among the vast number of individual reference.
It is very important to reduce the complexity, reduction of computation time and increase the fidelity of systems for pattern recognition.